To improve our understanding of social interaction in a developmental context it is important to know what the benefit of social interaction is. Once a collective of individuals is observed to exhibit social interaction, a post hoc analysis and explanation of its advantages is often straightforward. From an evolutionary or developmental point of view, however, the challenge is to identify a path that would lead towards such an interaction in the first place.

We start with the assumption of a single agent that generates its behaviour based on the driving forces of information maximization and information parsimony. This work is based on the recent advances in information theory based behaviour generation, especially Vergassola's idea of infotaxis.

Within a low-assumption agent-world interaction framework, based on Information Theory and Causal Bayesian Networks, we then demonstrate how every agent that needs to acquire relevant information in regard to its strategy selection will automatically inject part of this information back into the environment. We introduce the concept of 'Digested Information' which both quantifies, and explains this phenomenon. Based on the properties of digested information, especially the high density of relevant information in other agents actions, we outline how this could motivate the development of low level social interaction mechanisms, such as the ability to detect other agents.

This is a crucial first step towards understanding, how and why an agent's actions can be used to infer its belief about the world (in terms of a probability mapping of world states), and ultimately its goals and intentions (as terms of preferred states in the world state space).